Parallel Computing Toolbox?0 pages

Parallel Computing Toolbox
Perform parallel computations on multicore computers, GPUs, and computer clusters
Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore
processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and
parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI
programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel.
The toolbox provides twelve workers (MATLAB computational engines) to execute applications locally on a
multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid
computing service (using MATLAB Distributed Computing Server™). You can run parallel applications
interactively or in batch.
Built-in Parallel Computing Support in MathWorks Products
Key Features
▪ Parallel for-loops (parfor) for running task-parallel algorithms on multiple processors
▪ Support for CUDA-enabled NVIDIA GPUs
▪ Ability to run twelve workers locally on a multicore desktop
▪ Computer cluster and grid support (with MATLAB Distributed Computing Server)
▪ Interactive and batch execution of parallel applications
▪ Distributed arrays and single program multiple data (spmd) construct for large dataset handling and
data-parallel algorithms

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